MOSAIC ATM, INC. — National Aeronautics and Space Administration STTR Phase I: T10
MOSAIC ATM, INC. — STTR Phase I award from National Aeronautics and Space Administration.
Phase I STTR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from National Aeronautics and Space Administration in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $124,864. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code T10 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $124,864
- Agency
- National Aeronautics and Space Administration
- Program / Phase
- STTR · Phase I
- Topic
- T10
- Solicitation
- STTR_21_P1
- NAICS
- —
- Place of performance
- VA
- Period
- 2021-05-07 → 2022-06-19
Description
Mosaic ATM proposes an innovative approach to providing information about automated system trustworthiness in a given context, which will support humans in appropriately calibrating trust in such systems. Appropriately calibrated trust will, in turn, inform the scope of autonomy humans grant to the system to perform independent decision making and task execution. We communicate system trustworthiness through a combination of an innovative approach to explainable machine learning (ML) and representation of confidence in model results based on a quantification of uncertainty in those results given the available input data. We demonstrate our approach in the context of automated support for monitoring and managing crew wellbeing and performance in deep space exploration missions, where astronauts will be subject to the physical and psychological stress of performing in an isolated, confined, and extreme (ICE) environment. In Phase I, we will demonstrate our approach to support human assessment of automated system trustworthiness through a generalized method for explainable ML and representation of uncertainty in ML model results and situate them in a prototype system that will support evaluation of their effect on human calibration of trust. This prototype system will be based on our concept for automated support for monitoring and managing crew wellbeing and performance, which we will document in Phase I.